Physical Sciences and Mathematics Commons^™

Economic Challenges Facing Kentucky’S Electricity Generation Under Greenhouse Gas Constraints, Energy And Environment Cabinet, Commonwealth Of Kentucky, Department Of Statistics, University Of Kentucky, Center For Applied Energy Research, University Of Kentucky, Pacific Northwest National Laboratory

Statistics Reports

From the preface:

For the Energy and Environment Cabinet (EEC), which has primacy in administering most federal environmental laws and regulations at the state level, we have to understand the implications of what is arguably one of the most challenging issues to confront us—greenhouse gas (GHG) emissions and their impact on climate change. Efforts to reduce GHG or carbon dioxide (CO₂) emissions have moved beyond the point of discussion at the national level, and the United States Supreme Court has ruled that the U.S. Environmental Protection Agency (EPA) has the authority to regulate GHG emissions. Furthermore, while public …

Approximate Techniques In Solving Optimal Camera Placement Problems, Jian Zhao, Ruriko Yoshida, Sen-Ching Samson Cheung, David Haws

Statistics Faculty Publications

While the theoretical foundation of the optimal camera placement problem has been studied for decades, its practical implementation has recently attracted significant research interest due to the increasing popularity of visual sensor networks. The most flexible formulation of finding the optimal camera placement is based on a binary integer programming (BIP) problem. Despite the flexibility, most of the resulting BIP problems are NP-hard and any such formulations of reasonable size are not amenable to exact solutions. There exists a myriad of approximate algorithms for BIP problems, but their applications, efficiency, and scalability in solving camera placement are poorly understood. Thus, …

Combining Functions And The Closure Principle For Performing Follow-Up Tests In Functional Analysis Of Variance, Olga A. Vsevolozhskaya, Mark C. Greenwood, G. J. Bellante, S. L. Powell, R. L. Lawrence, K. S. Repasky

Olga A. Vsevolozhskaya

Functional analysis of variance involves testing for differences in functional means across kk groups in nn functional responses. If a significant overall difference in the mean curves is detected, one may want to identify the location of these differences. Cox and Lee (2008) proposed performing a point-wise test and applying the Westfall–Young multiple comparison correction. We propose an alternative procedure for identifying regions of significant difference in the functional domain. Our procedure is based on a region-wise test and application of a combining function along with the closure multiplicity adjustment principle. We give an explicit formulation of how to implement …

Use Of P-Values To Evaluate The Probability Of A Genuine Finding In Large-Scale Genetic Association Studies, Olga A. Vsevolozhskaya, Qing Lu, Chia-Ling Kuo, Dmitri V. Zaykin

Olga A. Vsevolozhskaya

To claim the existence of an association in modern genome-wide association studies (GWAS), a nominal P-value has to exceed a stringent Bonferroni-adjusted significance level. Despite strictness of the correction, a significant P-value does not indicate high probability that the claimed association is genuine. A simple Bayesian solution -- the False Positive Report Probability (FPRP) -- was previously proposed to convert the observed P-value to the corresponding probability of no true association. Although the FPRP solution is highly popular, it does not reflect probability that a particular finding is false. Here, we offer a simple POFIG method -- a Probability that …

Association Studies For Sequencing Data With Functional Analysis Of Variance, Olga A. Vsevolozhskaya, Mark C. Greenwood, Changshuai Wei, Qing Lu

Olga A. Vsevolozhskaya

The rapid development of next generation sequencing technologies and accompanying reduction in cost produce an increasing number of single nucleotide polymorphisms (SNPs) that can be identified across the genome. Analyzing high-dimensional genomic data is a challenge and requires development of new statistical methods. We propose to use the functional analysis of variance (FANOVA) to perform inference for sequencing data. FANOVA is used to test for differences in functional means of k groups over time. We suggest using FANOVA to test for a significant difference among SNPs between levels of a phenotype, such as the presence or absence of a disease. …

Risk Score Modeling Of Multiple Gene To Gene Interactions Using Aggregated-Multifactor Dimensionality Reduction, Hongying Dai, Richard J. Charnigo, Mara L. Becker, J. Steven Leeder, Alison A. Motsinger-Reif

Statistics Faculty Publications

BACKGROUND: Multifactor Dimensionality Reduction (MDR) has been widely applied to detect gene-gene (GxG) interactions associated with complex diseases. Existing MDR methods summarize disease risk by a dichotomous predisposing model (high-risk/low-risk) from one optimal GxG interaction, which does not take the accumulated effects from multiple GxG interactions into account.

RESULTS: We propose an Aggregated-Multifactor Dimensionality Reduction (A-MDR) method that exhaustively searches for and detects significant GxG interactions to generate an epistasis enriched gene network. An aggregated epistasis enriched risk score, which takes into account multiple GxG interactions simultaneously, replaces the dichotomous predisposing risk variable and provides higher resolution in the quantification …

A Neural Network Model To Translate Brain Developmental Events Across Mammalian Species, Radhakrishnan Nagarajan, Jeffrey N. Jonkman

Biostatistics Faculty Publications

Translating the timing of brain developmental events across mammalian species using suitable models has provided unprecedented insights into neural development and evolution. More importantly, these models can prove to be useful abstractions and predict unknown events across species from known empirical event timing data retrieved from published literature. Such predictions can be especially useful since the distribution of the event timing data is skewed with a majority of events documented only across a few selected species. The present study investigates the choice of single hidden layer feed-forward neural networks (FFNN) for predicting the unknown events from the empirical data. A …

Resampling-Based Multiple Comparison Procedure With Application To Point-Wise Testing With Functional Data, Olga A. Vsevolozhskaya, Mark C. Greenwood

Olga A. Vsevolozhskaya

No abstract provided.

The Psychological Impacts Of False Positive Ovarian Cancer Screening: Assessment Via Mixed And Trajectory Modeling, Amanda T. Wiggins

Theses and Dissertations--Epidemiology and Biostatistics

Ovarian cancer (OC) is the fifth most common cancer among women and has the highest mortality of any cancer of the female reproductive system. The majority (61%) of OC cases are diagnosed at a distant stage. Because diagnoses occur most commonly at a late-stage and prognosis for advanced disease is poor, research focusing on the development of effective OC screening methods to facilitate early detection in high-risk, asymptomatic women is fundamental in reducing OC-specific mortality. Presently, there is no screening modality proven efficacious in reducing OC-mortality. However, transvaginal ultrasonography (TVS) has shown value in early detection of OC. TVS presents …

Data Mining And Pattern Discovery Using Exploratory And Visualization Methods For Large Multidimensional Datasets, Hsin-Fang Li

Theses and Dissertations--Epidemiology and Biostatistics

Oral health problems have been a major public health concern profoundly affecting people’s general health and quality of life. Given that oral health data is composed of several measurable dimensions including clinical measurements, socio-behavioral factors, genetic predispositions, self-reported assessments, and quality of life measures, strategies for analyzing multidimensional data are neither computationally straightforward nor efficient. Researchers face major challenges to identify tools that circumvent the processes of manually probing the data.

The purpose of this dissertation is to provide applications of the proposed methodology on oral health-related data that go beyond identifying risk factors from a single dimension, and to …

Polytopes Arising From Binary Multi-Way Contingency Tables And Characteristic Imsets For Bayesian Networks, Jing Xi

Theses and Dissertations--Statistics

The main theme of this dissertation is the study of polytopes arising from binary multi-way contingency tables and characteristic imsets for Bayesian networks.

Firstly, we study on three-way tables whose entries are independent Bernoulli ran- dom variables with canonical parameters under no three-way interaction generalized linear models. Here, we use the sequential importance sampling (SIS) method with the conditional Poisson (CP) distribution to sample binary three-way tables with the sufficient statistics, i.e., all two-way marginal sums, fixed. Compared with Monte Carlo Markov Chain (MCMC) approach with a Markov basis (MB), SIS procedure has the advantage that it does not require …

Analysis Of Spatial Data, Xiang Zhang

Theses and Dissertations--Statistics

In many areas of the agriculture, biological, physical and social sciences, spatial lattice data are becoming increasingly common. In addition, a large amount of lattice data shows not only visible spatial pattern but also temporal pattern (see, Zhu et al. 2005). An interesting problem is to develop a model to systematically model the relationship between the response variable and possible explanatory variable, while accounting for space and time effect simultaneously.

Spatial-temporal linear model and the corresponding likelihood-based statistical inference are important tools for the analysis of spatial-temporal lattice data. We propose a general asymptotic framework for spatial-temporal linear models and …

James-Stein Type Compound Estimation Of Multiple Mean Response Functions And Their Derivatives, Limin Feng

Theses and Dissertations--Statistics

Charnigo and Srinivasan originally developed compound estimators to nonparametrically estimate mean response functions and their derivatives simultaneously when there is one response variable and one covariate. The compound estimator maintains self consistency and almost optimal convergence rate. This dissertation studies, in part, compound estimation with multiple responses and/or covariates. An empirical comparison of compound estimation, local regression and spline smoothing is included, and near optimal convergence rates are established in the presence of multiple covariates.

James and Stein proposed an estimator of the mean vector of a p dimensional multivariate normal distribution, which produces a smaller risk than the maximum …

Mapping And Decomposing Scale-Dependent Soil Moisture Variability Within An Inner Bluegrass Landscape, Carla Landrum

Theses and Dissertations--Plant and Soil Sciences

There is a shared desire among public and private sectors to make more reliable predictions, accurate mapping, and appropriate scaling of soil moisture and associated parameters across landscapes. A discrepancy often exists between the scale at which soil hydrologic properties are measured and the scale at which they are modeled for management purposes. Moreover, little is known about the relative importance of hydrologic modeling parameters as soil moisture fluctuates with time. More research is needed to establish which observation scales in space and time are optimal for managing soil moisture variation over large spatial extents and how these scales are …